@inproceedings{60b10b3a56c34375a05044a2eb9f5a69,
title = "An adaptive weighted degree kernel to predict the splice site",
abstract = "The weighted degree kernel is a good means to predict the splice site. Its prediction performance is affected by positions in the DNA sequence of nucleotide bases. Based on this fact, we propose confusing positions in this article. Using the confusing positions and the key positions which we proposed in previous work, we construct a weight array to obtain adaptive weighted degree kernel, a kind of string kernel to predict the splice site. Then to prove the efficient and advance of the method, we use the public available dataset to train support vector machines to compare the performance of the adaptive weighted degree kernel and conventional weighted degree kernel. The results show that the adaptive weighted degree kernel has better performance than the weighted degree kernel.",
keywords = "Adaptive weighted degree kernel, Confusing positions, Splice site prediction, Support vector machine, Weight array",
author = "Tianqi Wang and Ke Yan and Yong Xu and Jinxing Liu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 11th Chinese Conference on Biometric Recognition, CCBR 2016 ; Conference date: 14-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46654-5_81",
language = "English",
isbn = "9783319466538",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "739--746",
editor = "Shiguang Shan and Zhisheng You and Jie Zhou and Weishi Zheng and Yunhong Wang and Zhenan Sun and Jianjiang Feng and Qijun Zhao",
booktitle = "Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings",
address = "Germany",
}